Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f060346b390>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f0603391f28>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """  
    
    # Real images dimention
    real_dim = (image_width, image_height, image_channels)
    
    # Real images placeholder
    inputs_real = tf.placeholder(tf.float32, (None, *real_dim))
    
    # Generator input placeholder
    z = tf.placeholder(tf.float32, (None, z_dim))
    
    # Learning rate
    learning_rate = tf.placeholder(tf.float32, shape=())
    
    return inputs_real, z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False, alpha=0.1):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    
    with tf.variable_scope('discriminator', reuse=reuse):
     
        # First convolutional layer - 14 x 14 x 64
        conv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        conv1r = tf.maximum(alpha * conv1, conv1)
        
        # Second convolutional layer - 7 x 7 x 128
        conv2 = tf.layers.conv2d(conv1r, 128, 5, strides=2, padding='same')
        conv2n = tf.layers.batch_normalization(conv2, training=True)
        conv2r = tf.maximum(alpha * conv2n, conv2n)
        
        # Third convolutional layer - 4 x 4 x 256
        conv3 = tf.layers.conv2d(conv2r, 256, 5, strides=2, padding='same')
        conv3n = tf.layers.batch_normalization(conv3, training=True)
        conv3r = tf.maximum(alpha * conv3n, conv3n)
        
        # Fourth convolutional layer - 2 x 2 x 512
        conv4 = tf.layers.conv2d(conv3r, 512, 5, strides=2, padding='same')
        conv4n = tf.layers.batch_normalization(conv4, training=True)
        conv4r = tf.maximum(alpha * conv4n, conv4n)
                
        # Reshape output for the final layer
        reshape = tf.reshape(conv4r,(-1, 8 * 64 * 2 * 2))
        
        # Logits
        logits = tf.layers.dense(reshape, 1)
        
        # Output
        out = tf.sigmoid(logits)
     

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True, alpha=0.1):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse= not is_train):
        
        # Dense layer
        d = tf.layers.dense(z, 16 * 32 * 3 * 3)
        dr = tf.reshape(d, (-1, 3, 3, 16 * 32))
        drn = tf.layers.batch_normalization(dr, training=is_train)
        drnr = tf.maximum(alpha * drn, drn)
        
        # First transpose convolution - 7 x 7 x 128
        c1 = tf.layers.conv2d_transpose(drnr, 128, 3, strides=2, padding='valid')
        c1n = tf.layers.batch_normalization(c1, training=is_train)
        c1nr = tf.maximum(alpha * c1n, c1n)
        
        # Second transpose convolution - 14 x 14 x 64 
        c2 = tf.layers.conv2d_transpose(c1nr, 64, 5, strides=2, padding='same')
        c2n = tf.layers.batch_normalization(c2, training=is_train)
        c2nr = tf.maximum(alpha * c2n, c2n)
        
        # Third transpose convolution - 28 x 28 x 32
        c3 = tf.layers.conv2d_transpose(c2nr, 32, 5, strides=2, padding='same')
        c3n = tf.layers.batch_normalization(c3, training=is_train)
        c3nr = tf.maximum(alpha * c3n, c3n)
        
        # Fourth transpose convolution - 28 x 28 x out_channel_dim
        c4 = tf.layers.conv2d_transpose(c3nr, out_channel_dim, 5, strides=1, padding='same')
        
        # Output
        out = tf.tanh(c4)        
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    g_model = generator(input_z, out_channel_dim, is_train=True)
    
    # Real images from discriminator
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    
    # Fake images from discriminator
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    # Discriminator real images loss
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * 0.9))
    
    # Discriminator fake images loss
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    # Generator loss
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    # Discriminator loss
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Trainable variables
    t_vars = tf.trainable_variables()
    
    # Trainable discriminator variables
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    
    # Trainable generator variables
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    # Generator update
    gen_updates = [op for op in update_ops if op.name.startswith('generator')]
    
    # Optimizers
    with tf.control_dependencies(gen_updates):
        
        # Train optimizer for Discriminator
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        
        # Train optimizer for Generator
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """    
    # Number of color channels
    _, image_w, image_h, n_channels = data_shape
    
    # Model input
    img, z, lr = model_inputs(image_w, image_h, n_channels, z_dim)
    
    # Losses
    d_loss, g_loss = model_loss(img, z, n_channels)
    
    # Optimizers
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            
            # Set initial steps and sums
            steps = 0
            d_loss_sum = 0
            g_loss_sum = 0
            batch_count = 0
            
            for batch_images in get_batches(batch_size):
                
                steps += 1
                batch_count += 1
                batch_images * 2
                
                # Sample random noise for generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={img: batch_images, z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={z: batch_z, lr: learning_rate})

                # Update loss sums
                d_loss_sum += d_loss.eval({z: batch_z, img: batch_images})
                g_loss_sum += g_loss.eval({z: batch_z})

                # Print the losses
                if steps%20 == 0:
                    
                    # Generator output
                    show_generator_output(sess, 16, z, n_channels, data_image_mode)
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Avg. Discriminator Loss: {:.4f}...".format(d_loss_sum / batch_count),
                          "Avg. Generator Loss: {:.4f}".format(g_loss_sum / batch_count))   
                    
                    # Set loss sums back to zero
                    d_loss_sum = 0
                    g_loss_sum = 0
                    
                    # Set batch count back to zero
                    batch_count = 0

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2... Avg. Discriminator Loss: 3.4007... Avg. Generator Loss: 3.3678

Epoch 1/2... Avg. Discriminator Loss: 1.3669... Avg. Generator Loss: 1.9893

Epoch 1/2... Avg. Discriminator Loss: 0.6878... Avg. Generator Loss: 3.7131

Epoch 1/2... Avg. Discriminator Loss: 0.6831... Avg. Generator Loss: 2.5253

Epoch 1/2... Avg. Discriminator Loss: 1.3993... Avg. Generator Loss: 1.4498

Epoch 1/2... Avg. Discriminator Loss: 1.2050... Avg. Generator Loss: 2.1707

Epoch 1/2... Avg. Discriminator Loss: 0.7487... Avg. Generator Loss: 1.9073

Epoch 1/2... Avg. Discriminator Loss: 1.4217... Avg. Generator Loss: 2.2758

Epoch 1/2... Avg. Discriminator Loss: 0.4363... Avg. Generator Loss: 3.1914

Epoch 1/2... Avg. Discriminator Loss: 0.5785... Avg. Generator Loss: 4.5545

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 128
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/1... Avg. Discriminator Loss: 2.9227... Avg. Generator Loss: 2.7488

Epoch 1/1... Avg. Discriminator Loss: 0.9997... Avg. Generator Loss: 2.0173

Epoch 1/1... Avg. Discriminator Loss: 1.0211... Avg. Generator Loss: 2.3794

Epoch 1/1... Avg. Discriminator Loss: 0.9157... Avg. Generator Loss: 3.0156

Epoch 1/1... Avg. Discriminator Loss: 1.1263... Avg. Generator Loss: 2.3282

Epoch 1/1... Avg. Discriminator Loss: 0.8223... Avg. Generator Loss: 3.0106

Epoch 1/1... Avg. Discriminator Loss: 0.6914... Avg. Generator Loss: 2.8234

Epoch 1/1... Avg. Discriminator Loss: 0.6042... Avg. Generator Loss: 3.4402

Epoch 1/1... Avg. Discriminator Loss: 0.9579... Avg. Generator Loss: 2.4513

Epoch 1/1... Avg. Discriminator Loss: 0.4176... Avg. Generator Loss: 3.4109

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.